{"as_of":"2026-08-20T21:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e176bbda7a364dbcf0493fb53ce0873117f8f7f0ccf60f217b65adcd040f20c7","coverage":[{"denominator":34,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":34,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-11T11:50:26.030339Z","state":"measured"},{"denominator":34,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":34,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.08071/citation-record","integrity":"/paper/2607.08071/integrity","json":"/paper/2607.08071/citation-record.json","paper":"/paper/2607.08071"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2111.10952","last_updated":"2022-01-29T07:41:54Z","snapshot_observed_at":"2026-08-16T17:40:20.517357Z","submitted_at":"2021-11-22T02:34:46Z","title":"ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning","version":2},"cited_work":{"arxiv_id":"2111.10952","doi":"10.48550/arxiv.2111.10952","metadata_source":"pith","pith_arxiv_id":"2111.10952","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ext5: Towards extreme multi-task scaling for transfer learning","venue":"cs.CL","work_id":"39a693a1-b4d7-418b-b6a5-abd763bcbd54","year":2021},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/2111.10952","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:b3b18301f71ff06ab2b63b8539858aa8626c93d2e45ef7e6037c6926225d5cea","observation_id":"bb4fb850-c2f0-4df8-99b6-f7f92c53aef5","resolution":{"observed_at":"2026-07-10T00:56:41.127271Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T01:06:40.580448Z","title":null,"venue":null,"work_id":"4e79cd8f-21d7-478e-96c3-0c2d6659036d","year":null},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:41596c98ed216bac6e5b897f552cacd06d8625ef4f2dfa7d4c619bca71037b84","observation_id":"b2953f90-0a53-4ed1-aa93-b444446cc844","resolution":{"observed_at":"2026-07-10T01:06:40.581819Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.14165","last_updated":"2020-07-22T19:47:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-28T17:29:03Z","title":"Language Models are Few-Shot Learners","version":4},"cited_work":{"arxiv_id":"2005.14165","doi":"10.1145/1926385.1926423","metadata_source":"pith","pith_arxiv_id":"2005.14165","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Language Models are Few-Shot Learners","venue":"cs.CL","work_id":"214732c0-2edd-44a0-af9e-28184a2b8279","year":2020},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:c21d9b270ef2cbb782b6a03315a15e630921a5201009ace7484405cc59c62f2f","observation_id":"205f508e-451e-4528-a7f4-d4fef91ab6c4","resolution":{"observed_at":"2026-07-10T00:56:41.129762Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-07-09T08:48:33.42086+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-09T08:48:33.42086+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.02164","last_updated":"2020-03-03T05:33:49Z","snapshot_observed_at":"2026-08-13T14:14:59.271697Z","submitted_at":"2019-12-04T18:32:15Z","title":"Plug and Play Language Models: A Simple Approach to Controlled Text Generation","version":4},"cited_work":{"arxiv_id":"1912.02164","doi":"10.48550/arxiv.1912.02164","metadata_source":"pith","pith_arxiv_id":"1912.02164","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Yosinski, and Rosanne Liu","venue":"cs.CL","work_id":"0b303bb5-76b4-444d-bc87-1da9c1673b7a","year":2019},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/1912.02164","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:8be87e624017da7404ed24a1bb857d2a8d33c0e2cebf29714c022a8cd5e6bdf3","observation_id":"babaf92a-f2cc-43c0-91f2-dd0a924a26c7","resolution":{"observed_at":"2026-07-10T00:56:41.132236Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.03197","last_updated":"2019-10-15T11:25:06Z","snapshot_observed_at":"2026-08-15T22:15:14.213409Z","submitted_at":"2019-05-08T16:33:51Z","title":"Unified Language Model Pre-training for Natural Language Understanding and Generation","version":3},"cited_work":{"arxiv_id":"1905.03197","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.03197","snapshot_observed_at":"2026-07-10T20:47:34.544572Z","title":"Unified language model pre- training for natural language understanding and gen- eration","venue":"cs.CL","work_id":"84b9192c-f13f-4ccb-bf93-71f6b33d9b8e","year":2019},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/1905.03197","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:c9448bfec05689cfe2812a668b186d60dbdc8aa146000835a7fdd6d0da8dd81d","observation_id":"3e3380d1-9281-4ff6-82cb-0a5ba5c1bc6e","resolution":{"observed_at":"2026-07-10T00:56:41.159349Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-030-15719-7_11","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Lecture notes in computer science","work_id":"20ecea79-84f7-4089-a184-d3f6181f2d06","year":2019},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:6425dafa86de361053cdbd1d6b723a396818c68892cb743c7bd38da8d01e46ff","observation_id":"bded4870-5094-4b14-9230-785c792f7621","resolution":{"observed_at":"2026-07-10T00:56:40.893496Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-07-10T00:48:57.038204+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-10T00:48:57.038204+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.03654","last_updated":"2021-10-06T21:02:00Z","snapshot_observed_at":"2026-08-16T13:36:21.119762Z","submitted_at":"2020-06-05T19:54:34Z","title":"DeBERTa: Decoding-enhanced BERT with Disentangled Attention","version":6},"cited_work":{"arxiv_id":"2006.03654","doi":"10.48550/arxiv.2006.03654","metadata_source":"pith","pith_arxiv_id":"2006.03654","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DeBERTa: Decoding-enhanced BERT with Disentangled Attention","venue":"cs.CL","work_id":"cb3211f6-363f-4355-bb84-2ecfd7e78875","year":2020},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/2006.03654","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:ea55fdf5e54032aba9e291f6431d76fd205fd0a9f077f301a9d0106d31849b73","observation_id":"1ac9ed40-7c0f-45c5-8dba-592ca5a789c7","resolution":{"observed_at":"2026-07-10T00:56:41.119696Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1809.00794","last_updated":"2019-07-04T00:12:39Z","snapshot_observed_at":"2026-08-15T13:56:38.445381Z","submitted_at":"2018-09-04T04:40:34Z","title":"Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation","version":2},"cited_work":{"arxiv_id":"1809.00794","doi":null,"metadata_source":"pith","pith_arxiv_id":"1809.00794","snapshot_observed_at":"2026-07-10T00:56:41.115597Z","title":"Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation","venue":"cs.CL","work_id":"729d77c3-1593-42a6-a978-f871eab7e986","year":2018},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/1809.00794","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:82271d4f2480737946f5d4b3f9f56d6f3fc7f0382dcbf58a40c67e327c1417c6","observation_id":"1cf9f3bc-c469-4e92-b845-b5a3a67cbde2","resolution":{"observed_at":"2026-07-10T00:56:41.117214Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T01:06:40.582570Z","title":null,"venue":null,"work_id":"29d02846-8acd-44e6-8d3d-8d7a56de81fb","year":null},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:b129b30cec70d24049190a208e9c1df941d9aa6994d07a0728a823359d320f5b","observation_id":"d39928fa-c74f-4356-9dcc-13e1202364da","resolution":{"observed_at":"2026-07-10T01:06:40.583926Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1703.00955","last_updated":"2018-09-13T02:16:40Z","snapshot_observed_at":"2026-08-14T21:13:48.469616Z","submitted_at":"2017-03-02T21:23:47Z","title":"Toward Controlled Generation of Text","version":4},"cited_work":{"arxiv_id":"1703.00955","doi":null,"metadata_source":"pith","pith_arxiv_id":"1703.00955","snapshot_observed_at":"2026-07-10T00:56:41.123566Z","title":"Toward Controlled Generation of Text","venue":"cs.LG","work_id":"f16f4faf-a823-4682-8fb6-1cb692c44a58","year":2017},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/1703.00955","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:94db5291c8315c17f02f33e079eb974a743174e46194ce9090390af61001786c","observation_id":"62df4aa3-a7ce-4dea-bfd6-1c94ace56c37","resolution":{"observed_at":"2026-07-10T00:56:41.124788Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2500.333075","doi":"10.1145/3292500.3330751","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zajonc, R., and G","venue":null,"work_id":"8eb63a68-8b52-4b2e-95b6-428682815342","year":2019},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:073903330313fab489681e8e937a39234583e0151003993c1e483ddd1ccc688e","observation_id":"196ad541-5ab4-47f5-bcf1-36ada2a589a1","resolution":{"observed_at":"2026-07-10T00:56:40.899799Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.01980","last_updated":"2020-05-29T02:21:37Z","snapshot_observed_at":"2026-08-13T09:16:52.325506Z","submitted_at":"2020-04-04T17:24:47Z","title":"Hooks in the Headline: Learning to Generate Headlines with Controlled Styles","version":3},"cited_work":{"arxiv_id":"2004.01980","doi":null,"metadata_source":"pith","pith_arxiv_id":"2004.01980","snapshot_observed_at":"2026-07-10T00:56:41.150706Z","title":"Hooks in the Headline: Learning to Generate Headlines with Controlled Styles","venue":"cs.CL","work_id":"90098c6b-610a-4081-bcb5-af1d725c635f","year":2020},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/2004.01980","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:e8bb4e5d31fbef4a906d8b509dea4a925e6f27c4f84fa29e37c851c2408dee0a","observation_id":"66fa96b6-f64b-4857-ab44-bf3a63e1118e","resolution":{"observed_at":"2026-07-10T00:56:41.151847Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2021.naacl-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T00:56:40.890445Z","title":"In: Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cot- terell, R., Chakraborty, T., Zhou, Y","venue":null,"work_id":"f572b358-532b-4bcb-b458-9e97cf676246","year":2021},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:c15424907577aab6d8f14b600fdceaee0569d93b5ac8073cbf5e0bf3acbb7809","observation_id":"f8218fe5-d138-48e4-93bc-07fc2011eba3","resolution":{"observed_at":"2026-07-10T00:56:40.891757Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.05858","last_updated":"2019-09-20T20:08:56Z","snapshot_observed_at":"2026-08-19T21:50:02.108697Z","submitted_at":"2019-09-11T17:57:18Z","title":"CTRL: A Conditional Transformer Language Model for Controllable Generation","version":2},"cited_work":{"arxiv_id":"1909.05858","doi":null,"metadata_source":"pith","pith_arxiv_id":"1909.05858","snapshot_observed_at":"2026-07-10T11:37:03.420559Z","title":"CTRL: A Conditional Transformer Language Model for Controllable Generation","venue":"cs.CL","work_id":"c90b18cd-644d-4503-8eca-953249853cff","year":2019},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/1909.05858","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:9ae0c54b4e5a409c5279c27cc67a6ac121612706879f2dcb9c49b62297415b6e","observation_id":"24220d20-cb97-4e80-b7f1-903dc7bb12d4","resolution":{"observed_at":"2026-07-10T00:56:41.111662Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-17T19:26:44.032537Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":"1412.6980","doi":"10.1002/mrm.28086","metadata_source":"pith","pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Adam: A Method for Stochastic Optimization","venue":"cs.LG","work_id":"1910796d-9b52-4683-bf5c-de9632c1028b","year":2014},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:ff21c0046f6962a0580f4edc4385570647f7a2d9fa49a8d993fce304fdad235d","observation_id":"17bde010-16c0-4709-89e4-6e7ac07e0fc3","resolution":{"observed_at":"2026-07-10T00:56:41.161911Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.01850","last_updated":"2021-08-04T05:25:20Z","snapshot_observed_at":"2026-08-16T18:05:25.411453Z","submitted_at":"2021-08-04T05:25:20Z","title":"Controlled Text Generation as Continuous Optimization with Multiple Constraints","version":1},"cited_work":{"arxiv_id":"2108.01850","doi":null,"metadata_source":"pith","pith_arxiv_id":"2108.01850","snapshot_observed_at":"2026-07-10T00:56:41.112867Z","title":"Controlled Text Generation as Continuous Optimization with Multiple Constraints","venue":"cs.CL","work_id":"9f8fae45-aff4-4ee8-95a4-e01f34d04cc1","year":2021},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/2108.01850","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:bf709c53ce9bd3be16ce5bf565971fa43d415efd9b1533d3753da5e03aaadbee","observation_id":"3b35b5e2-f219-4609-a406-37488a6ee03b","resolution":{"observed_at":"2026-07-10T00:56:41.114084Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.13461","last_updated":"2019-10-29T18:01:00Z","snapshot_observed_at":"2026-07-06T08:33:12.534026Z","submitted_at":"2019-10-29T18:01:00Z","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","version":1},"cited_work":{"arxiv_id":"1910.13461","doi":"10.48550/arxiv.1910.13461","metadata_source":"pith","pith_arxiv_id":"1910.13461","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","venue":"cs.CL","work_id":"7ab72623-0d1b-41fc-96e9-181246f2ea00","year":2019},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/1910.13461","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:d555b135abdb16b2594c96c1ba69661418c8a121c915cfa49216d824c8374d23","observation_id":"7600f9dc-dc91-4f26-9335-03aef9f7bd8f","resolution":{"observed_at":"2026-07-10T00:56:41.122346Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T01:06:40.576820Z","title":null,"venue":null,"work_id":"d43e0a8a-318d-4512-981c-b04182ca8ffc","year":2004},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:137c047778e90975228835f87ee2a4c5d9f1abc5d72be969585f77163fde5c7f","observation_id":"37b516ff-31df-4e7b-a586-528f22dfebdb","resolution":{"observed_at":"2026-07-10T01:06:40.577956Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1808.10006","last_updated":"2018-08-31T21:59:35Z","snapshot_observed_at":"2026-08-14T18:35:26.897453Z","submitted_at":"2018-08-29T18:33:11Z","title":"Correcting Length Bias in Neural Machine Translation","version":2},"cited_work":{"arxiv_id":"1808.10006","doi":null,"metadata_source":"pith","pith_arxiv_id":"1808.10006","snapshot_observed_at":"2026-07-10T00:56:41.163251Z","title":"Correcting Length Bias in Neural Machine Translation","venue":"cs.CL","work_id":"af735184-2c77-4b2b-ac5c-6ac6978ddec4","year":2018},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/1808.10006","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:539739d70fb6f2e4cc0f37a774ac5d3a92edbfe8264bd2776e3827e595038b4c","observation_id":"a4bb2067-ad3e-4975-b0eb-c021dc7096bf","resolution":{"observed_at":"2026-07-10T00:56:41.164423Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.04063","last_updated":"2020-10-21T05:45:35Z","snapshot_observed_at":"2026-08-15T00:21:47.107771Z","submitted_at":"2020-01-13T05:12:38Z","title":"ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training","version":3},"cited_work":{"arxiv_id":"2001.04063","doi":null,"metadata_source":"pith","pith_arxiv_id":"2001.04063","snapshot_observed_at":"2026-07-10T00:56:41.142916Z","title":"ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training","venue":"cs.CL","work_id":"c73fa15a-7ff0-490f-b352-0e98baf6365e","year":2020},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/2001.04063","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:b8058cb8b724e4ec02b2eb93a3ebc5fefcf47881f0eebe93a681927de949d96f","observation_id":"a181349f-1196-4692-b148-9edb147a3d56","resolution":{"observed_at":"2026-07-10T00:56:41.144127Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.10683","last_updated":"2023-09-19T15:14:48Z","snapshot_observed_at":"2026-08-14T16:16:21.567225Z","submitted_at":"2019-10-23T17:37:36Z","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","version":4},"cited_work":{"arxiv_id":"1910.10683","doi":"10.18653/v1/2020.acl-main.259","metadata_source":"pith","pith_arxiv_id":"1910.10683","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","venue":"cs.LG","work_id":"50e3b368-0243-4726-8186-233869802ad1","year":2019},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/1910.10683","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:b31b471ac33400ae4f33c4144b234f14c678ca60908ba8bff5988ce45b5f7848","observation_id":"d99e5a36-149b-4fcd-96f0-a481a7192495","resolution":{"observed_at":"2026-07-10T00:56:41.146694Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1612.00563","last_updated":"2017-11-16T02:38:37Z","snapshot_observed_at":"2026-08-15T17:46:02.675837Z","submitted_at":"2016-12-02T04:37:22Z","title":"Self-critical Sequence Training for Image Captioning","version":2},"cited_work":{"arxiv_id":"1612.00563","doi":null,"metadata_source":"pith","pith_arxiv_id":"1612.00563","snapshot_observed_at":"2026-07-10T20:47:34.552818Z","title":"Self-critical Sequence Training for Image Captioning","venue":"cs.LG","work_id":"00e7ab0d-e525-45b0-9da9-2e19e3214ab1","year":2016},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/1612.00563","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:cf77374acfcc2209fb442d486112ec7d099b93c6cd8c5fac3ea32117b73d54b2","observation_id":"1d565fd9-7e3c-4012-8fd5-2a6eea650d05","resolution":{"observed_at":"2026-07-10T00:56:41.149194Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.12461","last_updated":"2020-04-16T13:29:28Z","snapshot_observed_at":"2026-08-19T02:03:36.612755Z","submitted_at":"2019-07-29T14:42:30Z","title":"Leveraging Pre-trained Checkpoints for Sequence Generation Tasks","version":2},"cited_work":{"arxiv_id":"1907.12461","doi":null,"metadata_source":"pith","pith_arxiv_id":"1907.12461","snapshot_observed_at":"2026-07-10T20:47:34.528322Z","title":"Leveraging Pre-trained Checkpoints for Sequence Generation Tasks","venue":"cs.CL","work_id":"0b695093-c6e9-4fd9-aecd-549160bc1faf","year":2019},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/1907.12461","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:f61c6d641dd49bbd4c53967ba757c98b254418b8ab7aa5a9c76dd87d784e54de","observation_id":"1a7a79b7-4b52-46a7-bc10-692c751c4f99","resolution":{"observed_at":"2026-07-10T00:56:41.154140Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1704.04368","last_updated":"2017-04-25T05:47:50Z","snapshot_observed_at":"2026-08-14T21:06:44.989948Z","submitted_at":"2017-04-14T07:55:19Z","title":"Get To The Point: Summarization with Pointer-Generator Networks","version":2},"cited_work":{"arxiv_id":"1704.04368","doi":null,"metadata_source":"pith","pith_arxiv_id":"1704.04368","snapshot_observed_at":"2026-07-10T00:56:41.140541Z","title":"Get To The Point: Summarization with Pointer-Generator Networks","venue":"cs.CL","work_id":"6974c78c-be8b-42ea-88c2-f72365d7be6c","year":2017},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/1704.04368","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:b8b494f8c8b03c7fe960d6a6fc0ee105137536504474932ef6f209c5280e1b8e","observation_id":"2bb5e116-1924-46de-b6ba-57588ffedaa0","resolution":{"observed_at":"2026-07-10T00:56:41.141747Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02450","last_updated":"2019-06-21T04:36:52Z","snapshot_observed_at":"2026-08-14T16:36:12.729672Z","submitted_at":"2019-05-07T10:13:04Z","title":"MASS: Masked Sequence to Sequence Pre-training for Language Generation","version":5},"cited_work":{"arxiv_id":"1905.02450","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.02450","snapshot_observed_at":"2026-07-10T00:56:41.135759Z","title":"MASS: Masked Sequence to Sequence Pre-training for Language Generation","venue":"cs.CL","work_id":"f6fedb38-f550-4a49-b0aa-748965fe4cb3","year":2019},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/1905.02450","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:0bb5023a4cb5cf4337a73f14636cad85013b76580472bced1d9dd6b0276aaf14","observation_id":"54328ce9-8ec2-4489-a886-b37259be4de6","resolution":{"observed_at":"2026-07-10T00:56:41.137110Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1609/aaai.v34i05","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T00:56:40.894034Z","title":"doi: 10.1609/AAAI.V34I05","venue":null,"work_id":"7ebdb886-9479-4d89-b0b6-9b70292b8c58","year":2020},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:b620c011518450e268e2f05b508b192a935386136f799f398ba245d3ae7fdd65","observation_id":"d993d505-d029-42f6-b41e-a5e0c2dba2c8","resolution":{"observed_at":"2026-07-10T00:56:40.895174Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-07-10T00:48:57.838726+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-10T00:48:57.838726+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.03762","last_updated":"2023-08-02T00:41:18Z","snapshot_observed_at":"2026-08-19T04:42:40.974785Z","submitted_at":"2017-06-12T17:57:34Z","title":"Attention Is All You Need","version":7},"cited_work":{"arxiv_id":"1706.03762","doi":"10.1186/s13550-021-00830-6","metadata_source":"pith","pith_arxiv_id":"1706.03762","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Attention Is All You Need","venue":"cs.CL","work_id":"baafb5a2-5272-43bc-932f-09fa9ffe5316","year":2017},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/1706.03762","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:30e9a0216778adb11e61f4c5c9b703c235f829d842f819a070a17f8974bda1c0","observation_id":"6336ea98-b98f-4947-8f32-5f9c7149fdde","resolution":{"observed_at":"2026-07-10T00:56:41.139376Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/bf00992696","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T20:47:34.373100Z","title":"Williams","venue":"Machine Learning","work_id":"469b3b81-55f9-4542-9dd7-570a63cfda74","year":1992},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:c5bf0b6205469c0f8d7c98c519f07e4d80597420236f68e3402b6ce7f03dd0ac","observation_id":"f3fa324f-173f-4c0f-b0a5-f1590952ea86","resolution":{"observed_at":"2026-07-10T00:56:40.896937Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-15T12:38:08.758899+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-15T12:38:08.758899+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.03771","last_updated":"2020-07-14T03:42:34Z","snapshot_observed_at":"2026-07-06T08:27:58.343233Z","submitted_at":"2019-10-09T03:23:22Z","title":"HuggingFace's Transformers: State-of-the-art Natural Language Processing","version":5},"cited_work":{"arxiv_id":"1910.03771","doi":"10.48550/arxiv.1910.03771","metadata_source":"pith","pith_arxiv_id":"1910.03771","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"HuggingFace's Transformers: State-of-the-art Natural Language Processing","venue":"cs.CL","work_id":"9d86da8d-01d3-41af-a0d2-ee14897927a9","year":2019},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/1910.03771","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:9d04e8d261e210821d7f0edb81c94335a4e9dc2d616996ae59294d5b9e7c0041","observation_id":"229ff287-6358-484d-98d9-b7eacad6fffa","resolution":{"observed_at":"2026-07-10T00:56:41.134593Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-07T21:38:08.865847+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-07T21:38:08.865847+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T01:06:40.574808Z","title":null,"venue":null,"work_id":"7531cb5c-1fda-4f94-870b-4c6d48076ef1","year":null},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:da0aff1bed48b00c889e106504d1897fbab94c2aa247514fa4093a12641fad20","observation_id":"1dda10d7-8e25-483b-8542-6125deefc821","resolution":{"observed_at":"2026-07-10T01:06:40.576214Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.03154","last_updated":"2021-12-06T16:41:26Z","snapshot_observed_at":"2026-08-16T17:36:42.646791Z","submitted_at":"2021-12-06T16:41:26Z","title":"VAE based Text Style Transfer with Pivot Words Enhancement Learning","version":1},"cited_work":{"arxiv_id":"2112.03154","doi":null,"metadata_source":"pith","pith_arxiv_id":"2112.03154","snapshot_observed_at":"2026-07-10T00:56:41.155593Z","title":"VAE based Text Style Transfer with Pivot Words Enhancement Learning","venue":"cs.CL","work_id":"21b0bdbb-c3a4-43cb-9e92-f8653f54a9bd","year":2021},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/2112.03154","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:6c0e01334465c8da20f3797b0b4f3c73adee4eff8089150758770de277763e55","observation_id":"72d6bb33-9db7-4f3f-a24a-fd4db391ba16","resolution":{"observed_at":"2026-07-10T00:56:41.156875Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.03582","last_updated":"2019-09-09T01:33:01Z","snapshot_observed_at":"2026-08-06T07:01:10.009759Z","submitted_at":"2019-09-09T01:33:01Z","title":"Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"1909.03582","doi":null,"metadata_source":"pith","pith_arxiv_id":"1909.03582","snapshot_observed_at":"2026-07-10T00:56:41.165585Z","title":"Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning","venue":"cs.CL","work_id":"ba43b25a-fc25-4b8e-b835-ac367cc0f6c2","year":2019},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"cited_paper":"/paper/1909.03582","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:3df2345a08d8ffbe03d469495df41b5ad6dd69faafef71f1b3c46ae9ebd82970","observation_id":"f93469be-025f-452e-a921-4a78c739fa57","resolution":{"observed_at":"2026-07-10T00:56:41.166801Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.05218","last_updated":"2021-08-15T18:34:33Z","snapshot_observed_at":"2026-08-19T21:13:01.711875Z","submitted_at":"2021-04-12T05:59:53Z","title":"FUDGE: Controlled Text Generation With Future Discriminators","version":2},"cited_work":{"arxiv_id":"2104.05218","doi":null,"metadata_source":"pith","pith_arxiv_id":"2104.05218","snapshot_observed_at":"2026-07-10T00:56:40.901016Z","title":"arXiv preprint arXiv:2104.05218 , year=","venue":"cs.CL","work_id":"6ef88816-726e-426d-8caf-c01d611b6c07","year":2021},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-11T11:50:26.030339Z"},"links":{"cited_paper":"/paper/2104.05218","citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:3ce7186e5895d5c0bd3976028601dc828a2e6a82511bb39b3788d3ef77603b8c","observation_id":"1cfc2586-6ed6-48b1-b6f2-b58b73a15fdd","resolution":{"observed_at":"2026-07-10T00:56:40.902367Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T01:06:40.578703Z","title":null,"venue":null,"work_id":"1f9daa99-5657-427e-b8a9-c2ca4335d5fb","year":2020},"citing_paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-10T00:46:41.020667Z"},"links":{"citing_paper":"/paper/2607.08071"},"observation_digest":"sha256:b45174935e55298ee820d963d0c8ea521316b389e1506d3456a060b4c3501521","observation_id":"ab2a44b9-e398-4235-a3a4-f578977a85bc","resolution":{"observed_at":"2026-07-10T01:06:40.579891Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2607.08071","last_updated":"2026-07-09T02:59:04Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-19T19:30:19.990322Z","submitted_at":"2026-07-09T02:59:04Z","title":"COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation"},"reference_resolution":{"displayed":34,"state_counts":{"malformed_identifier":2,"metadata_mismatch":5,"parse_uncertain":0,"unresolved":4,"verified_exact":23,"verified_fuzzy":0},"total_outbound_references":34},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2607.08071."}